Network flow stitching using middle box flow stitching

ABSTRACT

Systems, methods, and computer-readable media for flow stitching network traffic flow segments at a middlebox in a network environment. In some embodiments, a method can include collecting flow records of traffic flow segments at a middlebox in a network environment including one or more transaction identifiers assigned to the traffic flow segments. The traffic flow segments can correspond to one or more traffic flows passing through the middlebox and flow directions of the traffic flow segments with respect to the middlebox can be identified using the flow records. The traffic flow segments can be stitched together based on the one or more transaction identifiers and the flow directions of the traffic flow segments to form a stitched traffic flow of the one or more traffic flows passing through the middlebox. The stitched traffic flow can be incorporated as part of network traffic data for the network environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/621,925, filed on Jan. 25, 2018, entitled “Network Flow StitchingUsing Middle Box Flow Sensing,” the content of which is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

The present technology pertains to network traffic flow stitching andflow stitching network traffic flow segments at a middlebox in a networkenvironment.

BACKGROUND

Currently, sensors deployed in a network can be used to gather networktraffic data related to nodes operating in the network. The networktraffic data can include metadata relating to a packet, a collection ofpackets, a flow, a bidirectional flow, a group of flows, a session, or anetwork communication of another granularity. That is, the networktraffic data can generally include any information describingcommunication on all layers of the Open Systems Interconnection (OSI)model. For example, the network traffic data can includesource/destination MAC address, source/destination IP address, protocol,port number, etc. In some embodiments, the network traffic data can alsoinclude summaries of network activity or other network statistics suchas number of packets, number of bytes, number of flows, bandwidth usage,response time, latency, packet loss, jitter, and other networkstatistics.

Gathered network traffic data can be analyzed to provide insights intothe operation of the nodes in the network, otherwise referred to asanalytics. In particular, discovered application or inventories,application dependencies, policies, efficiencies, resource and bandwidthusage, and network flows can be determined for the network using thenetwork traffic data.

Sensors deployed in a network can be used to gather network traffic dataon a client and server level of granularity. For example, networktraffic data can be gathered for determining which clients arecommunicating which servers and vice versa. However, sensors are notcurrently deployed or integrated with systems to gather network trafficdata for different segments of traffic flows forming the traffic flowsbetween a server and a client. Specifically, current sensors gathernetwork traffic data as traffic flows directly between a client and aserver while ignoring which nodes, e.g. middleboxes, the traffic flowsactually pass through in passing between a server and a client. Thiseffectively treats the network environment between servers and clientsas a black box and leads to gaps in network traffic data and trafficflows indicated by the network traffic data.

In turn, such gaps in network traffic data and corresponding trafficflows can lead to deficiencies in diagnosing problems within a networkenvironment. For example, a problem stemming from an incorrectlyconfigured middlebox might be diagnosed as occurring at a client as theflow between the client and a server is treated as a black box. Inanother example, gaps in network traffic data between a server and aclient can lead to an inability to determine whether policies arecorrectly enforced at a middlebox between the server and the client.There therefore exist needs for systems, methods, and computer-readablemedia for generating network traffic data at nodes between servers andclients, e.g. at middleboxes between the servers and clients. Inparticular, there exist needs for systems, methods, andcomputer-readable media for stitching together traffic flows at nodesbetween servers and clients to generate a more complete and detailedtraffic flow, e.g. between the servers and the clients.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example network traffic monitoring system;

FIG. 2 illustrates an example of a network environment;

FIG. 3 depicts a diagram of an example network environment for stitchingtogether traffic flow segments between a client and a serve;

FIG. 4 illustrates a flowchart for an example method of stitchingtraffic flows passing through a middlebox;

FIG. 5 shows an example middlebox traffic flow stitching system;

FIG. 6 illustrates an example network device in accordance with variousembodiments; and

FIG. 7 illustrates an example computing device in accordance withvarious embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationscan be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which can beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms can be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles can be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

A method can include collecting flow records of traffic flow segments ata middlebox in a network environment corresponding to one or moretraffic flows passing through the middlebox. The flow records caninclude one or more transaction identifiers assigned to the traffic flowsegments. The method can include identifying flow directions of thetraffic flow segments in the network environment with respect to themiddlebox using the flow records. Further, the traffic flow segments canbe stitched together based on the one or more transaction identifiersassigned to the traffic flow segments and the flow directions of thetraffic flow segments in the network environment with respect to themiddlebox. Specifically, the traffic flow segments can be stitchedtogether to form a stitched traffic flow of the one or more trafficflows passing through the middlebox in the network environment. Themethod can also include incorporating the stitched traffic flow as partof network traffic data for the network environment.

A system can collect flow records of traffic flow segments at amiddblebox in a network environment corresponding to one or more trafficflows passing between a client and a server directly through themiddlebox. The flow records can include one or more transactionidentifiers assigned to the traffic flow segments. The system canidentify flow directions of the traffic flow segments in the networkenvironment with respect to the middlebox using the flow records.Further, the system can stitch together the traffic flow segmentstogether based on the one or more transaction identifiers assigned tothe traffic flow segments and the flow directions of the traffic flowsegments in the network environment with respect to the middlebox.Specifically, the traffic flow segments can be stitched together to forma stitched traffic flow of the one or more traffic flows passing throughthe middlebox in the network environment. The system can incorporate thestitched traffic flow as part of network traffic data for the networkenvironment.

A system can collect flow records of traffic flow segments at amiddlebox in a network environment corresponding to one or more trafficflows passing through the middlebox. The flow records can include one ormore transaction identifiers assigned to the traffic flow segments. Thesystem can identify flow directions of the traffic flow segments in thenetwork environment with respect to the middlebox using the flowrecords. Further, the system can stitch together the traffic flowsegments together based on the one or more transaction identifiersassigned to the traffic flow segments and the flow directions of thetraffic flow segments in the network environment with respect to themiddlebox. Specifically, the traffic flow segments can be stitchedtogether to form a stitched traffic flow of the one or more trafficflows passing through the middlebox in the network environment. Thesystem can incorporate the stitched traffic flows as part of anapplication dependency mapping included as part of network traffic datafor the network environment.

Example Embodiments

The disclosed technology addresses the need in the art for monitoringnetwork environments, e.g. to diagnose and prevent problems in thenetwork environment. The present technology involves system, methods,and computer-readable media for stitching together traffic flows atnodes between servers and clients to provide more detailed networktraffic data, e.g. for diagnosing and preventing problems in a networkenvironment.

The present technology will be described in the following disclosure asfollows. The discussion begins with an introductory discussion ofnetwork traffic data collection and a description of an example networktraffic monitoring system and an example network environment, as shownin FIGS. 1 and 2. A discussion of example systems and methods forstitching together network traffic flows, as shown in FIGS. 3-5, willthen follow. A discussion of example network devices and computingdevices, as illustrated in FIGS. 6 and 7, will then follow. Thedisclosure now turns to an introductory discussion of network sensordata collection based on network traffic flows and clustering of nodesin a network for purposes of collecting data based on network trafficflows.

Sensors implemented in networks are traditionally limited to collectingpacket data at networking devices. In some embodiments, networks can beconfigured with sensors at multiple points, including on networkingdevices (e.g., switches, routers, gateways, firewalls, deep packetinspectors, traffic monitors, load balancers, etc.), physical servers,hypervisors or shared kernels, virtual partitions (e.g., VMs orcontainers), and other network elements. This can provide a morecomprehensive view of the network. Further, network traffic data (e.g.,flows) can be associated with, or otherwise include, host and/orendpoint data (e.g., host/endpoint name, operating system, CPU usage,network usage, disk space, logged users, scheduled jobs, open files,information regarding files stored on a host/endpoint, etc.), processdata (e.g., process name, ID, parent process ID, path, CPU utilization,memory utilization, etc.), user data (e.g., user name, ID, login time,etc.), and other collectible data to provide more insight into networkactivity.

Sensors implemented in a network at multiple points can be used tocollect data for nodes grouped together into a cluster. Nodes can beclustered together, or otherwise a cluster of nodes can be identifiedusing one or a combination of applicable network operation factors. Forexample, endpoints performing similar workloads, communicating with asimilar set of endpoints or networking devices, having similar networkand security limitations (i.e., policies), and sharing other attributescan be clustered together.

In some embodiments, a cluster can be determined based on early fusionin which feature vectors of each node comprise the union of individualfeature vectors across multiple domains. For example, a feature vectorcan include a packet header-based feature (e.g., destination networkaddress for a flow, port, etc.) concatenated to an aggregate flow-basedfeature (e.g., the number of packets in the flow, the number of bytes inthe flow, etc.). A cluster can then be defined as a set of nodes whoserespective concatenated feature vectors are determined to exceedspecified similarity thresholds (or fall below specified distancethresholds).

In some embodiments, a cluster can be defined based on late fusion inwhich each node can be represented as multiple feature vectors ofdifferent data types or domains. In such systems, a cluster can be a setof nodes whose similarity (and/or distance measures) across differentdomains, satisfy specified similarity (and/or distance) conditions foreach domain. For example, a first node can be defined by a first networkinformation-based feature vector and a first process-based featurevector while a second node can be defined by a second networkinformation-based feature vector and a second process-based featurevector. The nodes can be determined to form a cluster if theircorresponding network-based feature vectors are similar to a specifieddegree and their corresponding process-based feature vectors are only aspecified distance apart.

Referring now to the drawings, FIG. 1 is an illustration of a networktraffic monitoring system 100 in accordance with an embodiment. Thenetwork traffic monitoring system 100 can include a configurationmanager 102, sensors 104, a collector module 106, a data mover module108, an analytics engine 110, and a presentation module 112. In FIG. 1,the analytics engine 110 is also shown in communication with out-of-banddata sources 114, third party data sources 116, and a network controller118.

The configuration manager 102 can be used to provision and maintain thesensors 104, including installing sensor software or firmware in variousnodes of a network, configuring the sensors 104, updating the sensorsoftware or firmware, among other sensor management tasks. For example,the sensors 104 can be implemented as virtual partition images (e.g.,virtual machine (VM) images or container images), and the configurationmanager 102 can distribute the images to host machines. In general, avirtual partition can be an instance of a VM, container, sandbox, orother isolated software environment. The software environment caninclude an operating system and application software. For softwarerunning within a virtual partition, the virtual partition can appear tobe, for example, one of many servers or one of many operating systemsexecuted on a single physical server. The configuration manager 102 caninstantiate a new virtual partition or migrate an existing partition toa different physical server. The configuration manager 102 can also beused to configure the new or migrated sensor.

The configuration manager 102 can monitor the health of the sensors 104.For example, the configuration manager 102 can request for statusupdates and/or receive heartbeat messages, initiate performance tests,generate health checks, and perform other health monitoring tasks. Insome embodiments, the configuration manager 102 can also authenticatethe sensors 104. For instance, the sensors 104 can be assigned a uniqueidentifier, such as by using a one-way hash function of a sensor's basicinput/out system (BIOS) universally unique identifier (UUID) and asecret key stored by the configuration image manager 102. The UUID canbe a large number that can be difficult for a malicious sensor or otherdevice or component to guess. In some embodiments, the configurationmanager 102 can keep the sensors 104 up to date by installing the latestversions of sensor software and/or applying patches. The configurationmanager 102 can obtain these updates automatically from a local sourceor the Internet.

The sensors 104 can reside on various nodes of a network, such as avirtual partition (e.g., VM or container) 120; a hypervisor or sharedkernel managing one or more virtual partitions and/or physical servers122, an application-specific integrated circuit (ASIC) 124 of a switch,router, gateway, or other networking device, or a packet capture (pcap)126 appliance (e.g., a standalone packet monitor, a device connected toa network devices monitoring port, a device connected in series along amain trunk of a datacenter, or similar device), or other element of anetwork. The sensors 104 can monitor network traffic between nodes, andsend network traffic data and corresponding data (e.g., host data,process data, user data, etc.) to the collectors 106 for storage. Forexample, the sensors 104 can sniff packets being sent over its hosts'physical or virtual network interface card (NIC), or individualprocesses can be configured to report network traffic and correspondingdata to the sensors 104. Incorporating the sensors 104 on multiple nodesand within multiple partitions of some nodes of the network can providefor robust capture of network traffic and corresponding data from eachhop of data transmission. In some embodiments, each node of the network(e.g., VM, container, or other virtual partition 120, hypervisor, sharedkernel, or physical server 122, ASIC 124, pcap 126, etc.) includes arespective sensor 104. However, it should be understood that varioussoftware and hardware configurations can be used to implement the sensornetwork 104.

As the sensors 104 capture communications and corresponding data, theycan continuously send network traffic data to the collectors 106. Thenetwork traffic data can include metadata relating to a packet, acollection of packets, a flow, a bidirectional flow, a group of flows, asession, or a network communication of another granularity. That is, thenetwork traffic data can generally include any information describingcommunication on all layers of the Open Systems Interconnection (OSI)model. For example, the network traffic data can includesource/destination MAC address, source/destination IP address, protocol,port number, etc. In some embodiments, the network traffic data can alsoinclude summaries of network activity or other network statistics suchas number of packets, number of bytes, number of flows, bandwidth usage,response time, latency, packet loss, jitter, and other networkstatistics.

The sensors 104 can also determine additional data, included as part ofgathered network traffic data, for each session, bidirectional flow,flow, packet, or other more granular or less granular networkcommunication. The additional data can include host and/or endpointinformation, virtual partition information, sensor information, processinformation, user information, tenant information, applicationinformation, network topology, application dependency mapping, clusterinformation, or other information corresponding to each flow.

In some embodiments, the sensors 104 can perform some preprocessing ofthe network traffic and corresponding data before sending the data tothe collectors 106. For example, the sensors 104 can remove extraneousor duplicative data or they can create summaries of the data (e.g.,latency, number of packets per flow, number of bytes per flow, number offlows, etc.). In some embodiments, the sensors 104 can be configured toonly capture certain types of network information and disregard therest. In some embodiments, the sensors 104 can be configured to captureonly a representative sample of packets (e.g., every 1,000th packet orother suitable sample rate) and corresponding data.

Since the sensors 104 can be located throughout the network, networktraffic and corresponding data can be collected from multiple vantagepoints or multiple perspectives in the network to provide a morecomprehensive view of network behavior. The capture of network trafficand corresponding data from multiple perspectives rather than just at asingle sensor located in the data path or in communication with acomponent in the data path, allows the data to be correlated from thevarious data sources, which can be used as additional data points by theanalytics engine 110. Further, collecting network traffic andcorresponding data from multiple points of view ensures more accuratedata is captured. For example, a conventional sensor network can belimited to sensors running on external-facing network devices (e.g.,routers, switches, network appliances, etc.) such that east-westtraffic, including VM-to-VM or container-to-container traffic on a samehost, may not be monitored. In addition, packets that are dropped beforetraversing a network device or packets containing errors cannot beaccurately monitored by the conventional sensor network. The sensornetwork 104 of various embodiments substantially mitigates or eliminatesthese issues altogether by locating sensors at multiple points ofpotential failure. Moreover, the network traffic monitoring system 100can verify multiple instances of data for a flow (e.g., source endpointflow data, network device flow data, and endpoint flow data) against oneanother.

In some embodiments, the network traffic monitoring system 100 canassess a degree of accuracy of flow data sets from multiple sensors andutilize a flow data set from a single sensor determined to be the mostaccurate and/or complete. The degree of accuracy can be based on factorssuch as network topology (e.g., a sensor closer to the source can bemore likely to be more accurate than a sensor closer to thedestination), a state of a sensor or a node hosting the sensor (e.g., acompromised sensor/node can have less accurate flow data than anuncompromised sensor/node), or flow data volume (e.g., a sensorcapturing a greater number of packets for a flow can be more accuratethan a sensor capturing a smaller number of packets).

In some embodiments, the network traffic monitoring system 100 canassemble the most accurate flow data set and corresponding data frommultiple sensors. For instance, a first sensor along a data path cancapture data for a first packet of a flow but can be missing data for asecond packet of the flow while the situation is reversed for a secondsensor along the data path. The network traffic monitoring system 100can assemble data for the flow from the first packet captured by thefirst sensor and the second packet captured by the second sensor.

As discussed, the sensors 104 can send network traffic and correspondingdata to the collectors 106. In some embodiments, each sensor can beassigned to a primary collector and a secondary collector as part of ahigh availability scheme. If the primary collector fails orcommunications between the sensor and the primary collector are nototherwise possible, a sensor can send its network traffic andcorresponding data to the secondary collector. In other embodiments, thesensors 104 are not assigned specific collectors but the network trafficmonitoring system 100 can determine an optimal collector for receivingthe network traffic and corresponding data through a discovery process.In such embodiments, a sensor can change where it sends it networktraffic and corresponding data if its environments changes, such as if adefault collector fails or if the sensor is migrated to a new locationand it would be optimal for the sensor to send its data to a differentcollector. For example, it can be preferable for the sensor to send itsnetwork traffic and corresponding data on a particular path and/or to aparticular collector based on latency, shortest path, monetary cost(e.g., using private resources versus a public resources provided by apublic cloud provider), error rate, or some combination of thesefactors. In other embodiments, a sensor can send different types ofnetwork traffic and corresponding data to different collectors. Forexample, the sensor can send first network traffic and correspondingdata related to one type of process to one collector and second networktraffic and corresponding data related to another type of process toanother collector.

The collectors 106 can be any type of storage medium that can serve as arepository for the network traffic and corresponding data captured bythe sensors 104. In some embodiments, data storage for the collectors106 is located in an in-memory database, such as dashDB from IBM®,although it should be appreciated that the data storage for thecollectors 106 can be any software and/or hardware capable of providingrapid random access speeds typically used for analytics software. Invarious embodiments, the collectors 106 can utilize solid state drives,disk drives, magnetic tape drives, or a combination of the foregoingaccording to cost, responsiveness, and size requirements. Further, thecollectors 106 can utilize various database structures such as anormalized relational database or a NoSQL database, among others.

In some embodiments, the collectors 106 can only serve as networkstorage for the network traffic monitoring system 100. In suchembodiments, the network traffic monitoring system 100 can include adata mover module 108 for retrieving data from the collectors 106 andmaking the data available to network clients, such as the components ofthe analytics engine 110. In effect, the data mover module 108 can serveas a gateway for presenting network-attached storage to the networkclients. In other embodiments, the collectors 106 can perform additionalfunctions, such as organizing, summarizing, and preprocessing data. Forexample, the collectors 106 can tabulate how often packets of certainsizes or types are transmitted from different nodes of the network. Thecollectors 106 can also characterize the traffic flows going to and fromvarious nodes. In some embodiments, the collectors 106 can match packetsbased on sequence numbers, thus identifying traffic flows and connectionlinks. As it can be inefficient to retain all data indefinitely incertain circumstances, in some embodiments, the collectors 106 canperiodically replace detailed network traffic data with consolidatedsummaries. In this manner, the collectors 106 can retain a completedataset describing one period (e.g., the past minute or other suitableperiod of time), with a smaller dataset of another period (e.g., theprevious 2-10 minutes or other suitable period of time), andprogressively consolidate network traffic and corresponding data ofother periods of time (e.g., day, week, month, year, etc.). In someembodiments, network traffic and corresponding data for a set of flowsidentified as normal or routine can be winnowed at an earlier period oftime while a more complete data set can be retained for a lengthierperiod of time for another set of flows identified as anomalous or as anattack.

The analytics engine 110 can generate analytics using data collected bythe sensors 104. Analytics generated by the analytics engine 110 caninclude applicable analytics of nodes or a cluster of nodes operating ina network. For example, analytics generated by the analytics engine 110can include one or a combination of information related to flows of datathrough nodes, detected attacks on a network or nodes of a network,applications at nodes or distributed across the nodes, applicationdependency mappings for applications at nodes, policies implemented atnodes, and actual policies enforced at nodes.

Computer networks can be exposed to a variety of different attacks thatexpose vulnerabilities of computer systems in order to compromise theirsecurity. Some network traffic can be associated with malicious programsor devices. The analytics engine 110 can be provided with examples ofnetwork states corresponding to an attack and network statescorresponding to normal operation. The analytics engine 110 can thenanalyze network traffic and corresponding data to recognize when thenetwork is under attack. In some embodiments, the network can operatewithin a trusted environment for a period of time so that the analyticsengine 110 can establish a baseline of normal operation. Since malwareis constantly evolving and changing, machine learning can be used todynamically update models for identifying malicious traffic patterns.

In some embodiments, the analytics engine 110 can be used to identifyobservations which differ from other examples in a dataset. For example,if a training set of example data with known outlier labels exists,supervised anomaly detection techniques can be used. Supervised anomalydetection techniques utilize data sets that have been labeled as normaland abnormal and train a classifier. In a case in which it is unknownwhether examples in the training data are outliers, unsupervised anomalytechniques can be used. Unsupervised anomaly detection techniques can beused to detect anomalies in an unlabeled test data set under theassumption that the majority of instances in the data set are normal bylooking for instances that seem to fit to the remainder of the data set.

The analytics engine 110 can include a data lake 130, an applicationdependency mapping (ADM) module 140, and elastic processing engines 150.The data lake 130 is a large-scale storage repository that providesmassive storage for various types of data, enormous processing power,and the ability to handle nearly limitless concurrent tasks or jobs. Insome embodiments, the data lake 130 is implemented using the Hadoop®Distributed File System (HDFS™) from Apache® Software Foundation ofForest Hill, Md. HDFS™ is a highly scalable and distributed file systemthat can scale to thousands of cluster nodes, millions of files, andpetabytes of data. HDFS™ is optimized for batch processing where datalocations are exposed to allow computations to take place where the dataresides. HDFS™ provides a single namespace for an entire cluster toallow for data coherency in a write-once, read-many access model. Thatis, clients can only append to existing files in the node. In HDFS™,files are separated into blocks, which are typically 64 MB in size andare replicated in multiple data nodes. Clients access data directly fromdata nodes.

In some embodiments, the data mover 108 receives raw network traffic andcorresponding data from the collectors 106 and distributes or pushes thedata to the data lake 130. The data lake 130 can also receive and storeout-of-band data 114, such as statuses on power levels, networkavailability, server performance, temperature conditions, cage doorpositions, and other data from internal sources, and third party data116, such as security reports (e.g., provided by Cisco® Systems, Inc. ofSan Jose, Calif., Arbor Networks® of Burlington, Mass., Symantec® Corp.of Sunnyvale, Calif., Sophos® Group plc of Abingdon, England, Microsoft®Corp. of Seattle, Wash., Verizon® Communications, Inc. of New York,N.Y., among others), geolocation data, IP watch lists, Whois data,configuration management database (CMDB) or configuration managementsystem (CMS) as a service, and other data from external sources. Inother embodiments, the data lake 130 can instead fetch or pull rawtraffic and corresponding data from the collectors 106 and relevant datafrom the out-of-band data sources 114 and the third party data sources116. In yet other embodiments, the functionality of the collectors 106,the data mover 108, the out-of-band data sources 114, the third partydata sources 116, and the data lake 130 can be combined. Variouscombinations and configurations are possible as would be known to one ofordinary skill in the art.

Each component of the data lake 130 can perform certain processing ofthe raw network traffic data and/or other data (e.g., host data, processdata, user data, out-of-band data or third party data) to transform theraw data to a form useable by the elastic processing engines 150. Insome embodiments, the data lake 130 can include repositories for flowattributes 132, host and/or endpoint attributes 134, process attributes136, and policy attributes 138. In some embodiments, the data lake 130can also include repositories for VM or container attributes,application attributes, tenant attributes, network topology, applicationdependency maps, cluster attributes, etc.

The flow attributes 132 relate to information about flows traversing thenetwork. A flow is generally one or more packets sharing certainattributes that are sent within a network within a specified period oftime. The flow attributes 132 can include packet header fields such as asource address (e.g., Internet Protocol (IP) address, Media AccessControl (MAC) address, Domain Name System (DNS) name, or other networkaddress), source port, destination address, destination port, protocoltype, class of service, among other fields. The source address cancorrespond to a first endpoint (e.g., network device, physical server,virtual partition, etc.) of the network, and the destination address cancorrespond to a second endpoint, a multicast group, or a broadcastdomain. The flow attributes 132 can also include aggregate packet datasuch as flow start time, flow end time, number of packets for a flow,number of bytes for a flow, the union of TCP flags for a flow, amongother flow data.

The host and/or endpoint attributes 134 describe host and/or endpointdata for each flow, and can include host and/or endpoint name, networkaddress, operating system, CPU usage, network usage, disk space, ports,logged users, scheduled jobs, open files, and information regardingfiles and/or directories stored on a host and/or endpoint (e.g.,presence, absence, or modifications of log files, configuration files,device special files, or protected electronic information). Asdiscussed, in some embodiments, the host and/or endpoints attributes 134can also include the out-of-band data 114 regarding hosts such as powerlevel, temperature, and physical location (e.g., room, row, rack, cagedoor position, etc.) or the third party data 116 such as whether a hostand/or endpoint is on an IP watch list or otherwise associated with asecurity threat, Whois data, or geocoordinates. In some embodiments, theout-of-band data 114 and the third party data 116 can be associated byprocess, user, flow, or other more granular or less granular networkelement or network communication.

The process attributes 136 relate to process data corresponding to eachflow, and can include process name (e.g., bash, httpd, netstat, etc.),ID, parent process ID, path (e.g., /usr2/username/bin/, /usr/local/bin,/usr/bin, etc.), CPU utilization, memory utilization, memory address,scheduling information, nice value, flags, priority, status, start time,terminal type, CPU time taken by the process, the command that startedthe process, and information regarding a process owner (e.g., user name,ID, user's real name, e-mail address, user's groups, terminalinformation, login time, expiration date of login, idle time, andinformation regarding files and/or directories of the user).

The policy attributes 138 contain information relating to networkpolicies. Policies establish whether a particular flow is allowed ordenied by the network as well as a specific route by which a packettraverses the network. Policies can also be used to mark packets so thatcertain kinds of traffic receive differentiated service when used incombination with queuing techniques such as those based on priority,fairness, weighted fairness, token bucket, random early detection, roundrobin, among others. The policy attributes 138 can include policystatistics such as a number of times a policy was enforced or a numberof times a policy was not enforced. The policy attributes 138 can alsoinclude associations with network traffic data. For example, flows foundto be non-conformant can be linked or tagged with corresponding policiesto assist in the investigation of non-conformance.

The analytics engine 110 can include any number of engines 150,including for example, a flow engine 152 for identifying flows (e.g.,flow engine 152) or an attacks engine 154 for identify attacks to thenetwork. In some embodiments, the analytics engine can include aseparate distributed denial of service (DDoS) attack engine 155 forspecifically detecting DDoS attacks. In other embodiments, a DDoS attackengine can be a component or a sub-engine of a general attacks engine.In some embodiments, the attacks engine 154 and/or the DDoS engine 155can use machine learning techniques to identify security threats to anetwork. For example, the attacks engine 154 and/or the DDoS engine 155can be provided with examples of network states corresponding to anattack and network states corresponding to normal operation. The attacksengine 154 and/or the DDoS engine 155 can then analyze network trafficdata to recognize when the network is under attack. In some embodiments,the network can operate within a trusted environment for a time toestablish a baseline for normal network operation for the attacks engine154 and/or the DDoS.

The analytics engine 110 can further include a search engine 156. Thesearch engine 156 can be configured, for example to perform a structuredsearch, an NLP (Natural Language Processing) search, or a visual search.Data can be provided to the engines from one or more processingcomponents.

The analytics engine 110 can also include a policy engine 158 thatmanages network policy, including creating and/or importing policies,monitoring policy conformance and non-conformance, enforcing policy,simulating changes to policy or network elements affecting policy, amongother policy-related tasks.

The ADM module 140 can determine dependencies of applications of thenetwork. That is, particular patterns of traffic can correspond to anapplication, and the interconnectivity or dependencies of theapplication can be mapped to generate a graph for the application (i.e.,an application dependency mapping). In this context, an applicationrefers to a set of networking components that provides connectivity fora given set of workloads. For example, in a conventional three-tierarchitecture for a web application, first endpoints of the web tier,second endpoints of the application tier, and third endpoints of thedata tier make up the web application. The ADM module 140 can receiveinput data from various repositories of the data lake 130 (e.g., theflow attributes 132, the host and/or endpoint attributes 134, theprocess attributes 136, etc.). The ADM module 140 can analyze the inputdata to determine that there is first traffic flowing between externalendpoints on port 80 of the first endpoints corresponding to HypertextTransfer Protocol (HTTP) requests and responses. The input data can alsoindicate second traffic between first ports of the first endpoints andsecond ports of the second endpoints corresponding to application serverrequests and responses and third traffic flowing between third ports ofthe second endpoints and fourth ports of the third endpointscorresponding to database requests and responses. The ADM module 140 candefine an ADM for the web application as a three-tier applicationincluding a first EPG comprising the first endpoints, a second EPGcomprising the second endpoints, and a third EPG comprising the thirdendpoints.

The presentation module 112 can include an application programminginterface (API) or command line interface (CLI) 160, a securityinformation and event management (STEM) interface 162, and a webfront-end 164. As the analytics engine 110 processes network traffic andcorresponding data and generates analytics data, the analytics data maynot be in a human-readable form or it can be too voluminous for a userto navigate. The presentation module 112 can take the analytics datagenerated by analytics engine 110 and further summarize, filter, andorganize the analytics data as well as create intuitive presentationsfor the analytics data.

In some embodiments, the API or CLI 160 can be implemented using Hadoop®Hive from Apache® for the back end, and Java® Database Connectivity(JDBC) from Oracle® Corporation of Redwood Shores, Calif., as an APIlayer. Hive is a data warehouse infrastructure that provides datasummarization and ad hoc querying. Hive provides a mechanism to querydata using a variation of structured query language (SQL) that is calledHiveQL. JDBC is an API for the programming language Java®, which defineshow a client can access a database.

In some embodiments, the SIEM interface 162 can be implemented usingHadoop® Kafka for the back end, and software provided by Splunk®, Inc.of San Francisco, Calif. as the SIEM platform. Kafka is a distributedmessaging system that is partitioned and replicated. Kafka uses theconcept of topics. Topics are feeds of messages in specific categories.In some embodiments, Kafka can take raw packet captures and telemetryinformation from the data mover 108 as input, and output messages to aSIEM platform, such as Splunk®. The Splunk® platform is utilized forsearching, monitoring, and analyzing machine-generated data.

In some embodiments, the web front-end 164 can be implemented usingsoftware provided by MongoDB®, Inc. of New York, N.Y. and Hadoop®ElasticSearch from Apache® for the back-end, and Ruby on Rails™ as theweb application framework. MongoDB® is a document-oriented NoSQLdatabase based on documents in the form of JavaScript® Object Notation(JSON) with dynamic schemas. ElasticSearch is a scalable and real-timesearch and analytics engine that provides domain-specific language (DSL)full querying based on JSON. Ruby on Rails™ is model-view-controller(MVC) framework that provides default structures for a database, a webservice, and web pages. Ruby on Rails™ relies on web standards such asJSON or extensible markup language (XML) for data transfer, andhypertext markup language (HTML), cascading style sheets, (CSS), andJavaScript® for display and user interfacing.

Although FIG. 1 illustrates an example configuration of the variouscomponents of a network traffic monitoring system, those of skill in theart will understand that the components of the network trafficmonitoring system 100 or any system described herein can be configuredin a number of different ways and can include any other type and numberof components. For example, the sensors 104, the collectors 106, thedata mover 108, and the data lake 130 can belong to one hardware and/orsoftware module or multiple separate modules. Other modules can also becombined into fewer components and/or further divided into morecomponents.

FIG. 2 illustrates an example of a network environment 200 in accordancewith an embodiment. In some embodiments, a network traffic monitoringsystem, such as the network traffic monitoring system 100 of FIG. 1, canbe implemented in the network environment 200. It should be understoodthat, for the network environment 200 and any environment discussedherein, there can be additional or fewer nodes, devices, links,networks, or components in similar or alternative configurations.Embodiments with different numbers and/or types of clients, networks,nodes, cloud components, servers, software components, devices, virtualor physical resources, configurations, topologies, services, appliances,deployments, or network devices are also contemplated herein. Further,the network environment 200 can include any number or type of resources,which can be accessed and utilized by clients or tenants. Theillustrations and examples provided herein are for clarity andsimplicity.

The network environment 200 can include a network fabric 202, a Layer 2(L2) network 204, a Layer 3 (L3) network 206, and servers 208 a, 208 b,208 c, 208 d, and 208 e (collectively, 208). The network fabric 202 caninclude spine switches 210 a, 210 b, 210 c, and 210 d (collectively,“210”) and leaf switches 212 a, 212 b, 212 c, 212 d, and 212 e(collectively, “212”). The spine switches 210 can connect to the leafswitches 212 in the network fabric 202. The leaf switches 212 caninclude access ports (or non-fabric ports) and fabric ports. The fabricports can provide uplinks to the spine switches 210, while the accessports can provide connectivity to endpoints (e.g., the servers 208),internal networks (e.g., the L2 network 204), or external networks(e.g., the L3 network 206).

The leaf switches 212 can reside at the edge of the network fabric 202,and can thus represent the physical network edge. For instance, in someembodiments, the leaf switches 212 d and 212 e operate as border leafswitches in communication with edge devices 214 located in the externalnetwork 206. The border leaf switches 212 d and 212 e can be used toconnect any type of external network device, service (e.g., firewall,deep packet inspector, traffic monitor, load balancer, etc.), or network(e.g., the L3 network 206) to the fabric 202.

Although the network fabric 202 is illustrated and described herein asan example leaf-spine architecture, one of ordinary skill in the artwill readily recognize that various embodiments can be implemented basedon any network topology, including any datacenter or cloud networkfabric. Indeed, other architectures, designs, infrastructures, andvariations are contemplated herein. For example, the principlesdisclosed herein are applicable to topologies including three-tier(including core, aggregation, and access levels), fat tree, mesh, bus,hub and spoke, etc. Thus, in some embodiments, the leaf switches 212 canbe top-of-rack switches configured according to a top-of-rackarchitecture. In other embodiments, the leaf switches 212 can beaggregation switches in any particular topology, such as end-of-row ormiddle-of-row topologies. In some embodiments, the leaf switches 212 canalso be implemented using aggregation switches.

Moreover, the topology illustrated in FIG. 2 and described herein isreadily scalable and can accommodate a large number of components, aswell as more complicated arrangements and configurations. For example,the network can include any number of fabrics 202, which can begeographically dispersed or located in the same geographic area. Thus,network nodes can be used in any suitable network topology, which caninclude any number of servers, virtual machines or containers, switches,routers, appliances, controllers, gateways, or other nodesinterconnected to form a large and complex network. Nodes can be coupledto other nodes or networks through one or more interfaces employing anysuitable wired or wireless connection, which provides a viable pathwayfor electronic communications.

Network communications in the network fabric 202 can flow through theleaf switches 212. In some embodiments, the leaf switches 212 canprovide endpoints (e.g., the servers 208), internal networks (e.g., theL2 network 204), or external networks (e.g., the L3 network 206) accessto the network fabric 202, and can connect the leaf switches 212 to eachother. In some embodiments, the leaf switches 212 can connect endpointgroups (EPGs) to the network fabric 202, internal networks (e.g., the L2network 204), and/or any external networks (e.g., the L3 network 206).EPGs are groupings of applications, or application components, and tiersfor implementing forwarding and policy logic. EPGs can allow forseparation of network policy, security, and forwarding from addressingby using logical application boundaries. EPGs can be used in the networkenvironment 200 for mapping applications in the network. For example,EPGs can comprise a grouping of endpoints in the network indicatingconnectivity and policy for applications.

As discussed, the servers 208 can connect to the network fabric 202 viathe leaf switches 212. For example, the servers 208 a and 208 b canconnect directly to the leaf switches 212 a and 212 b, which can connectthe servers 208 a and 208 b to the network fabric 202 and/or any of theother leaf switches. The servers 208 c and 208 d can connect to the leafswitches 212 b and 212 c via the L2 network 204. The servers 208 c and208 d and the L2 network 204 make up a local area network (LAN). LANscan connect nodes over dedicated private communications links located inthe same general physical location, such as a building or campus.

The WAN 206 can connect to the leaf switches 212 d or 212 e via the L3network 206. WANs can connect geographically dispersed nodes overlong-distance communications links, such as common carrier telephonelines, optical light paths, synchronous optical networks (SONET), orsynchronous digital hierarchy (SDH) links. LANs and WANs can include L2and/or L3 networks and endpoints.

The Internet is an example of a WAN that connects disparate networksthroughout the world, providing global communication between nodes onvarious networks. The nodes typically communicate over the network byexchanging discrete frames or packets of data according to predefinedprotocols, such as the Transmission Control Protocol/Internet Protocol(TCP/IP). In this context, a protocol can refer to a set of rulesdefining how the nodes interact with each other. Computer networks canbe further interconnected by an intermediate network node, such as arouter, to extend the effective size of each network. The endpoints,e.g. the servers 208, can include any communication device or component,such as a computer, server, blade, hypervisor, virtual machine,container, process (e.g., running on a virtual machine), switch, router,gateway, host, device, external network, etc.

In some embodiments, the network environment 200 also includes a networkcontroller running on the host 208 a. The network controller isimplemented using the Application Policy Infrastructure Controller(APIC™) from Cisco®. The APIC™ provides a centralized point ofautomation and management, policy programming, application deployment,and health monitoring for the fabric 202. In some embodiments, the APIC™is operated as a replicated synchronized clustered controller. In otherembodiments, other configurations or software-defined networking (SDN)platforms can be utilized for managing the fabric 202.

In some embodiments, a physical server 208 can have instantiated thereona hypervisor 216 for creating and running one or more virtual switches(not shown) and one or more virtual machines 218, as shown for the host208 b. In other embodiments, physical servers can run a shared kernelfor hosting containers. In yet other embodiments, the physical server208 can run other software for supporting other virtual partitioningapproaches. Networks in accordance with various embodiments can includeany number of physical servers hosting any number of virtual machines,containers, or other virtual partitions. Hosts can also compriseblade/physical servers without virtual machines, containers, or othervirtual partitions, such as the servers 208 a, 208 c, 208 d, and 208 e.

The network environment 200 can also integrate a network trafficmonitoring system, such as the network traffic monitoring system 100shown in FIG. 1. For example, the network traffic monitoring system ofFIG. 2 includes sensors 220 a, 220 b, 220 c, and 220 d (collectively,“220”), collectors 222, and an analytics engine, such as the analyticsengine 110 of FIG. 1, executing on the server 208 e. The analyticsengine can receive and process network traffic data collected by thecollectors 222 and detected by the sensors 220 placed on nodes locatedthroughout the network environment 200. Although the analytics engine208 e is shown to be a standalone network appliance in FIG. 2, it willbe appreciated that the analytics engine 208 e can also be implementedas a virtual partition (e.g., VM or container) that can be distributedonto a host or cluster of hosts, software as a service (SaaS), or othersuitable method of distribution. In some embodiments, the sensors 220run on the leaf switches 212 (e.g., the sensor 220 a), the hosts (e.g.,the sensor 220 b), the hypervisor 216 (e.g., the sensor 220 c), and theVMs 218 (e.g., the sensor 220 d). In other embodiments, the sensors 220can also run on the spine switches 210, virtual switches, serviceappliances (e.g., firewall, deep packet inspector, traffic monitor, loadbalancer, etc.) and in between network elements. In some embodiments,sensors 220 can be located at each (or nearly every) network componentto capture granular packet statistics and data at each hop of datatransmission. In other embodiments, the sensors 220 may not be installedin all components or portions of the network (e.g., shared hostingenvironment in which customers have exclusive control of some virtualmachines).

As shown in FIG. 2, a host can include multiple sensors 220 running onthe host (e.g., the host sensor 220 b) and various components of thehost (e.g., the hypervisor sensor 220 c and the VM sensor 220 d) so thatall (or substantially all) packets traversing the network environment200 can be monitored. For example, if one of the VMs 218 running on thehost 208 b receives a first packet from the WAN 206, the first packetcan pass through the border leaf switch 212 d, the spine switch 210 b,the leaf switch 212 b, the host 208 b, the hypervisor 216, and the VM.Since all or nearly all of these components contain a respective sensor,the first packet will likely be identified and reported to one of thecollectors 222. As another example, if a second packet is transmittedfrom one of the VMs 218 running on the host 208 b to the host 208 d,sensors installed along the data path, such as at the VM 218, thehypervisor 216, the host 208 b, the leaf switch 212 b, and the host 208d will likely result in capture of metadata from the second packet.

Currently, sensors, e.g. such as those of the network traffic monitoringsystem 100, deployed in a network can be used to gather network trafficdata related to nodes operating in the network. The network traffic datacan include metadata relating to a packet, a collection of packets, aflow, a bidirectional flow, a group of flows, a session, or a networkcommunication of another granularity. That is, the network traffic datacan generally include any information describing communication on alllayers of the Open Systems Interconnection (OSI) model. For example, thenetwork traffic data can include source/destination MAC address,source/destination IP address, protocol, port number, etc. In someembodiments, the network traffic data can also include summaries ofnetwork activity or other network statistics such as number of packets,number of bytes, number of flows, bandwidth usage, response time,latency, packet loss, jitter, and other network statistics.

Gathered network traffic data can be analyzed to provide insights intothe operation of the nodes in the network, otherwise referred to asanalytics. In particular, discovered application or inventories,application dependencies, policies, efficiencies, resource and bandwidthusage, and network flows can be determined for the network using thenetwork traffic data.

Sensors deployed in a network can be used to gather network traffic dataon a client and server level of granularity. For example, networktraffic data can be gathered for determining which clients arecommunicating which servers and vice versa. However, sensors are notcurrently deployed or integrated with systems to gather network trafficdata for different segments of traffic flows forming the traffic flowsbetween a server and a client. Specifically, current sensors gathernetwork traffic data as traffic flows directly between a client and aserver while ignoring which nodes, e.g. middleboxes, the traffic flowsactually pass through in passing between a server and a client. Thiseffectively treats the network environment between servers and clientsas a black box and leads to gaps in or otherwise incomplete networktraffic data and traffic flows indicated by the network traffic data.

In turn, such gaps in network traffic data and corresponding trafficflows can lead to deficiencies in diagnosing problems within a networkenvironment. For example, a problem stemming from an incorrectlyconfigured middlebox might be diagnosed as occurring at a client as theflow between the client and a server is treated as a black box. Inanother example, gaps in network traffic data between a server and aclient can lead to an inability to determine whether policies arecorrectly enforced at a middlebox between the server and the client.There therefore exist needs for systems, methods, and computer-readablemedia for generating network traffic data at nodes between servers andclients, e.g. at middleboxes between the servers and clients. Inparticular, there exist needs for systems, methods, andcomputer-readable media for stitching together traffic flows at nodesbetween servers and clients to generate a more complete and detailedtraffic flow, e.g. between the servers and the clients.

The present includes systems, methods, and computer-readable media forstitching traffic flow segments at a middlebox in a network environmentto form a stitched traffic flow through the middlebox in the networkenvironment. In particular flow records of traffic flow segments at amiddlebox corresponding to one or more traffic flows passing through themiddlebox can be collected. The traffic flows can pass through themiddlebox between a client and a server. The flow records can includeone or more transaction identifiers assigned to the traffic flowsegments. Subsequently, flow directions of the traffic flow segments inthe network environment with respect to the middlebox can be identifiedusing the flow records. The traffic flow segments can be stitchedtogether to form a stitched traffic flow of the one or more trafficflows based on the one or more transaction identifiers and the flowdirections of the traffic flow segments. The stitched traffic flow canthen be incorporated as part of network traffic data for the networkenvironment.

FIG. 3 depicts a diagram of an example network environment 300 forstitching together traffic flow segments between a client and a server.The network environment 300 shown in FIG. 3 includes a client 302, aserver 304 and a middlebox 306. The client 302 and the server 304 canexchange data. More specifically, the client 302 and the server 304 canexchange data as part of the client 302 accessing network services inthe network environment 300 and as part of the server 304 providing theclient 302 access to network services in the network environment 300.For example, the client 302 can send a request for data that canultimately be delivered to the server 304 and the server 304 canultimately send the data back to the client 302 as part of a reply tothe request.

The client 302 and the server 304 can exchange data as part of one ormore traffic flows. A traffic flow can be unidirectional orbidirectional. For example, a traffic flow can include the client 302sending a request that is ultimately received at the server 304. Viceversa, a traffic flow can include the server 304 sending a response thatis ultimately received at the client 302. In another example, a trafficflow can include both the client 302 sending a request that isultimately received at the server 304 and the server 304 sending aresponse to the request that is ultimately received at the client 302.Traffic flows between the client 302 and the server 304 can form part ofa traffic flow including the client 302 and other sources/destinationsat different network nodes, e.g. separate from the server 304 and theclient 302, within a network environment. For example, traffic flowsbetween the client 302 and the server 304 can form part of an overalltraffic flow between the client 302 and a network node within a networkenvironment that is ultimately accessed through the server 304 and anetwork fabric.

In the example network environment 300 shown in FIG. 3, the client 302and the server 304 can exchange data or otherwise communicate throughthe middlebox 306. A middlebox, as used herein, is an applicablenetworking device for controlling network traffic in the networkenvironment 300 that passes through the middlebox. More specifically, amiddlebox can be an applicable networking device for filtering,inspecting, modifying, or otherwise controlling traffic that passesthrough the middlebox for purposes other than actually forwarding thetraffic to an intended destination. For example, a middlebox can includea firewall, an intrusion detection system, a network address translator,a WAN optimizer, and a load balancer.

In supporting exchange of data between the client 302 and the server304, different portions of traffic flows, otherwise referred to astraffic flow segments, can be created at the middlebox 306 between theclient 302 and the server 304. Specifically, the middlebox 306 canreceive data from the client 302 in a first traffic flow segment 308-1.Subsequently, the middlebox 306 can provide data received from theclient 302, e.g. through the first traffic flow segment 308-1, to theserver 304 as part of a second traffic flow segment 308-2. Similarly,the middlebox 306 can receive data from the server 304 in a thirdtraffic flow segment 308-3. Subsequently, the middlebox 306 can providedata received from the server 304, e.g. in the third traffic flowsegment 308-3, to the client 302 as part of a fourth traffic flowsegment 308-4.

All or an applicable combination of the first traffic flow segment308-1, the second traffic flow segment 308-2, the third traffic flowsegment 308-3, and the fourth traffic flow segment 308-4, (collectivelyreferred to as the “traffic flow segments 308”) can form part of asingle traffic flow. For example, the first traffic flow segment 308-1and the second traffic flow segment 308-2 can form a request transmittedfrom the client 302 to the server 304 and combine to form a singletraffic flow between the client 302 and the server 304. In anotherexample, the first and second traffic flow segments 308-1 and 308-2 canform a request transmitted to the server 304 and the third and fourthtraffic flow segments 308-3 and 308-4 can form a response to therequest. Further in the example, the traffic flow segments 308 includingboth the request and the response to the request can form a singletraffic flow between the client 302 and the server 304.

The traffic flow segments 308 can be associated with or otherwiseassigned one or more transaction identifiers. More specifically, atransaction identifier can be uniquely associated with a single trafficflow passing through the middlebox 306. Subsequently, all or acombination of the traffic flow segments 308 can be associated with atransaction identifier uniquely associated with a traffic flow formed byall or the combination of the traffic flow segments 308. For example,the traffic flow segments 308 can form a single traffic flow between theclient 302 and the server 304 and each be assigned a single transactionidentifier for the traffic flow. In another example, the first trafficflow segment 308-1 and the second traffic flow segment 308-2 can form afirst traffic flow and the third traffic flow segment 308-3 and thefourth traffic flow segment 308-4 can form a second traffic flow.Subsequently, a transaction identifier uniquely associated with thefirst traffic flow can be assigned to the first traffic flow segment308-1 and the second traffic flow segment 308-2, while a transactionidentifier uniquely associated with the second traffic flow can beassigned to the third traffic flow segment 308-3 and the fourth trafficflow segment 308-4.

While the client 302 and the server 304 are shown as communicatingthrough the middlebox 306, in the example environment shown in FIG. 3,either or both of the client 302 and the server 304 can be replaced withanother middlebox. For example, the server 304 and another middlebox cancommunicate with each other through the middlebox 306. In anotherexample, the client 302 and another middlebox can communicate with eachother through the middlebox 306.

The example network environment shown in FIG. 3 includes a middleboxtraffic flow segment collector 310 and a middlebox traffic flowstitching system 312. The middlebox traffic flow segment collector 310functions to collect flow records for the middlebox 306. In collectingflow records for the middlebox 306, the middlebox traffic flow segmentcollector 310 can be implemented as an appliance. Further, the middleboxtraffic flow segment collector 310 can be implemented, at least in part,at the middlebox 306. Additionally, the middlebox traffic flow segmentcollector 310 can be implemented, at least in part, remote from themiddlebox 306. For example, the middlebox traffic flow segment collector310 can be implemented on a virtual machine either residing at themiddlebox 306 or remote from the middlebox 306.

Flow records of a middlebox can include applicable data related totraffic segments flowing through the middlebox. Specifically, flowrecords of a middlebox can include one or a combination of a source ofdata transmitted in a traffic flow segment, a destination for datatransmitted in a traffic flow segment, a transaction identifier assignedto a traffic flow segment. More specifically, flow records of amiddlebox can include one or a combination of an address, e.g. IPaddress of a source or a destination, and an identification of a port ata source or a destination, e.g. an ephemeral port, a virtual IP (hereinreferred to as “VIP”) port, a subnet IP (herein referred to as “SNIP”)port, or a server port. For example, flow records collected by themiddlebox traffic flow segment collector 310 for the first traffic flowsegment 308-1 and the second traffic flow segment 308-2 can include aunique identifier associated with a traffic flow formed by the segments308-1 and 308-2 and assigned to the first and second traffic flowsegments 308-1 and 308-2. Further in the example, the flow records caninclude an IP address of the client 302 where the first traffic flowsegment 308-1 originates and a VIP port at the middlebox 306 where thefirst traffic flow segment 308-1 is received. Still further in theexample, the flow records can include an SNIP port at the middlebox 306where the second traffic flow segment 308-2 originates and a server portwhere the second traffic flow segment 308-2 is sent to for purposes ofload balancing.

Data included in flow records of corresponding traffic flow segmentspassing through a middlebox depend on whether the traffic flow segmentsoriginate at the middlebox or end at the middlebox. For example, a flowrecord for the first traffic flow segment 308-1 that is collected by themiddlebox traffic flow segment collector 310 can include a uniquetransaction identifier and indicate that the first traffic flow segmentstarts at the client 302 and ends at the middlebox 306. Similarly, aflow record for the second traffic flow segment 308-2 can include theunique transaction identifier, which is also assigned to the firsttraffic flow segment 308-1, as well as an indication that the secondtraffic flow segment starts at the middlebox 306 and ends at the server304. Accordingly, flow records for traffic flow segments passing throughthe middlebox 306 are each rooted at the middlebox 306, e.g. byincluding an indication of the middlebox as a source or a destination ofthe traffic flow segments. Specifically, as traffic flow segments arerooted at the middlebox 306, the flow records for traffic flow segmentspassing through the middlebox 306 each either begin or end at themiddlebox 306.

The middlebox 306 can generate flow records for traffic flow segmentspassing through the middlebox 306. More specifically, the middlebox 306can associate or otherwise assign a unique transaction identifier totraffic flow segments as part of creating flow records for the trafficflow segments. For example, the middlebox 306 can assign a TID1 of aconsumer request to the first traffic flow segment 308-1 as part ofcreating a flow record, e.g. for the first traffic flow segment 308-1.Further in the example, the middlebox 306 can determine to send theconsumer request to the specific server 304, e.g. as part of loadbalancing. Still further in the example, the middlebox 306 can assignthe TID1 of the consumer request to the second traffic flow segment308-1 as the consumer request is transmitted to the server 304 throughthe second traffic flow segment 308-2, e.g. as part of the loadbalancing. Subsequently, the middlebox 306 can export the generated flowsegments for traffic flow segments passing through the middlebox 306.

Additionally, the middlebox 306 can modify a flow record for a trafficflow segment by associating the traffic flow segment with a transactionidentifier as part of exporting the flow record. For example, themiddlebox 306 can determine to export a flow record for a traffic flowsegment. Subsequently, before exporting the flow record, the middlebox306 can associate the traffic flow segment with a transaction identifierand subsequently modify the flow record to include the transactionidentifier. The middlebox 306 can then export the modified flow recordincluding the transaction identifier.

The middlebox traffic flow segment collector 310 can collect flowrecords from the middlebox 306 as the flow records are completed orotherwise generated by the middlebox 306. Specifically, the middlebox306 can generate and/or export flow records for traffic flow segments asall or portions of corresponding traffic flows actually pass through themiddlebox 306. More specifically, the middlebox 306 can create andexport traffic flow records as either or both the first traffic flowsegment 308-1 and the second traffic flow segment 308-2 are completed atthe middlebox 306. Additionally, the middlebox 306 can generate and/orexport flow records for traffic flow segments once a correspondingtraffic flow formed by the segments is completed through the middlebox306. For example, the middlebox 306 can create and export traffic flowrecords for the traffic flow segments 308 once all of the traffic flowsegments 308 are transmitted to complete a traffic flow through themiddlebox 306. Further in the example, the middlebox 306 can recognizethat a consumer to producer flow is complete, e.g. the first and trafficflow segments 308-1 and 308-2 are complete or all of the traffic flowsegments 308 are completed, and subsequently the middlebox 306 canexport one or more corresponding flow records to the middlebox trafficflow segment collector 310.

The middlebox traffic flow segment collector 310 can receive orotherwise collect traffic flow records from the middlebox 306 accordingto an applicable protocol for exporting flow records, e.g. from amiddlebox. More specifically, the middlebox 306 can export flow recordsto the middlebox traffic flow segment collector 310 according to anapplicable protocol for exporting flow records, e.g. from a middlebox.For example, the middlebox 306 can export flow records to the middleboxtraffic flow segment collector 310 using an Internet Protocol FlowInformation Export (herein referred to as “IPFIX”) protocol. In anotherexample, the middlebox 306 can export flow records to the middleboxtraffic flow segment collector 310 using a NetFlow Packet transportprotocol.

While flow records can indicate traffic flow segments are rooted at themiddlebox 306, the flow records for traffic segments passing through themiddlebox 306 can fail to link the traffic flow segments, e.g. throughthe middlebox 306. Specifically, flow records for the first traffic flowsegment 308-1 can indicate that the first traffic flow segment 308-1ends at the middlebox 306 while flow records for the second traffic flowsegment 308-2 can indicate that the second traffic flow segment 308-2begins at the middlebox 306, while failing to link the first and secondtraffic flow segments 308-1 and 308-2. This is problematic insynchronizing or otherwise identifying how the server 304 and the client302 communicate through the middlebox 306. Specifically, failing to linktraffic flow segments through the middlebox 306 leads to a view from aserver-side perspective that all flows end in the middlebox 306.Similarly, failing to link traffic flow segments through the middlebox306 leads to a view from a client-side perspective that all flows end inthe middlebox 306. This can correspond to gaps in mapping traffic flowsbetween the client 302 and the server 304, e.g. the middlebox 306 istreated like a black box without linking the client 302 with the server304. In turn, this can lead to deficiencies in diagnosing problemswithin the network environment 300. For example, a failed policy checkat the middlebox 306 can mistakenly be identified as happening at theclient 302 even though it actually occurs at the middlebox 306.Specifically, the failed policy check can be triggered by a failure ofthe middlebox 306 to route data according to the policy between theclient 302 and the server 304, however since the traffic flow segmentsbetween the middlebox 306 and the client 302 are not linked with thetraffic flow segments between the middlebox 306 and the server 304, thefailed policy check can be identified from a traffic flow segment asoccurring at the client 302 instead of the middlebox 306.

The middlebox traffic flow stitching system 312 functions to stitchtogether traffic flow segments passing through the middlebox 306 tocreate a stitched traffic flow at the middlebox 306. For example, themiddlebox traffic flow stitching system 312 can stitch together thefirst traffic flow segment 308-1, the second traffic flow segment 308-2,the third traffic flow segment 308-3, and the fourth traffic flowsegment 308-4 to form a stitched traffic flow. Stitched traffic flowscan be represented or otherwise used to create corresponding flow data.Flow data for a stitched traffic flow can include identifiers ofstitched traffic flow segments, e.g. identifiers of sources anddestinations of the traffic flow segments, and transactions associatedwith the stitched traffic flow segments, e.g. associated transactionidentifiers.

In stitching together traffic flow segments at the middlebox 306 tocreate a stitched traffic flow, the middlebox 306 no longer functions asa black box with respect to traffic flows passing through the middlebox306. Specifically, from both a server side perspective and a client sideperspective, a traffic flow can be viewed as actually passing throughthe middlebox 306 to the client 302 or the server 304 and not just astraffic flow segments that only originate at or end at the middlebox306. More specifically, the traffic flow can be viewed as a completedtraffic flow through the middlebox 306 instead of merely beginning at orending at the middlebox 306. This is advantageous as it allows for morecomplete and insightful network monitoring, leading to more accurateproblem diagnosing and solving. For example, as traffic flows are seenas actually passing through the middlebox 306, middlebox 306misconfigurations can be identified from the traffic flows, e.g. as partof monitoring network environments.

Traffic flows at the middlebox 306 stitched together by the middleboxtraffic flow stitching system 312 can be used to enforce policies atmiddleboxes, including the middlebox 306. Specifically, stitched trafficflows generated by the middlebox traffic flow stitching system 312 canbe used to identify dependencies between either or both servers andclients. For example, stitched traffic flows generated by the middleboxtraffic flow stitching system 312 can be used to generate applicationdependency mappings between different applications at servers andclients. Subsequently, policies can be set and subsequently enforced atthe middlebox 306 based on dependencies identified using stitchedtraffic flows generated by the middlebox traffic flow stitching system312. For example, a policy to load balance communications betweenclients and servers can be identified according to an applicationdependency mapping between the clients and the servers identifiedthrough stitched traffic flows at the middlebox 306. Further in theexample, the policy can subsequently be enforced at the middlebox 306 toprovide load balancing between the clients and the servers.

The middlebox traffic flow stitching system 312 can stitch togethertraffic flow segments passing through the middlebox 306 based on flowrecords collected from the middlebox 306. Specifically, the middleboxtraffic flow stitching system 312 can stitch together traffic flowsegments based on flow records collected by the middlebox traffic flowsegment collector 310 from the middlebox 306. In using flow records tostitch together traffic flow segments, the middlebox traffic flowstitching system 312 can stitch together traffic flow segments based ontransaction identifiers assigned to the traffic flow segments, asindicated by the flow records. Specifically, the middlebox traffic flowstitching system 312 can stitch together traffic flow segments that areassigned the same transaction identifiers. For example, the traffic flowsegments 308 can all have the same assigned transaction identifier, andthe middlebox traffic flow stitching system 312 can stitch together thetraffic flow segments 308 to form a stitched traffic flow based on theshared transaction identifier.

Further, the middlebox traffic flow stitching system 312 can identifyflow directions of traffic flow segments passing through the middlebox306 using flow records collected from the middlebox 306 by the middleboxtraffic flow segment collector 310. Specifically, the middlebox trafficflow stitching system 312 can identify flow directions of traffic flowsegments with respect to the middlebox 306 using flow records collectedfrom the middlebox 306. For example, the middlebox traffic flowstitching system 312 can use flow records from the middlebox 306 toidentify the fourth traffic flow segment 308-4 flows from the middlebox306 to the client 302. The middlebox traffic flow stitching system 312can use identified sources and destinations of traffic flow segments, asindicated by flow records, to identify flow directions of the trafficflow segments. For example, the middlebox traffic flow stitching system312 can determine the first traffic flow segment 308-1 flows from theclient 302 to the middlebox 306 based on an identification of a clientIP address as the source of the first traffic flow segment 308-1 and anidentification of a VIP port at the middlebox 306.

In stitching together traffic flow segments based on flow records, themiddlebox traffic flow stitching system 312 can stitch together thetraffic flow segments based on directions of the traffic flow segmentsidentified from the flow records. For example, the middlebox trafficflow stitching system 312 can stitch together the first traffic flowsegment 308-1 with the second traffic flow segment 308-2 based on theidentified direction of the first and second traffic flow segments 308-1and 308-2 from the client 302 towards the server 304. Additionally, institching together traffic flow segments based on flow records, themiddlebox traffic flow stitching system 312 can stitch together thetraffic flow segments based on directions of the traffic flow segmentsand also transaction identifiers assigned to the traffic flow segments.For example, the middlebox traffic flow stitching system 312 can stitchthe third and fourth traffic flow segments 308-3 and 308-4 togetherbased on the segments having the same transaction identifier and theshared direction of the segments from the server 304 to the client 302.

The middlebox traffic flow stitching system 312 can stitch togethertraffic flow segments in an order based on flow directions of thetraffic flow segments. More specifically, the middlebox traffic flowstitching system 312 can use a shared transaction identifier todetermine traffic flow segments to stitch together, and stitch thetraffic flow segments in a specific order based on flow directions ofthe traffic flow segments to form a stitched traffic flow. For example,the middlebox traffic flow stitching system 312 can determine to stitchthe traffic flow segments 308 based on a shared transaction identifierassigned to the traffic flow segments 308. Further in the example, themiddlebox traffic flow stitching system 312 can determine to stitch thesecond traffic flow segment 308-2 after the first traffic flow segment308-1, stitch the third traffic flow segment 308-3 after the secondtraffic flow segment 308-2, and stitch the third traffic flow segment308-4 after the third traffic flow segment 308-2, based on correspondingidentified flow directions of the flow segments 308, e.g. with respectto the middlebox 306.

The middlebox traffic flow stitching system 312 can incorporate stitchedtraffic flows through the middlebox 306 as part of network traffic datafor the network environment. For example, the middlebox traffic flowstitching system 312 can include stitched traffic flows through themiddlebox 306 with other traffic flows in the network environment, e.g.from servers to nodes in a network fabric. The middlebox traffic flowstitching system 312 can incorporate stitched traffic flows as part ofnetwork traffic data generated by an applicable network trafficmonitoring system, such as the network traffic monitoring system 100shown in FIG. 1. In incorporating stitched traffic flows with networktraffic data generated by a network traffic monitoring system, all orportions of the middlebox traffic flow stitching system 312 can beintegrated at the network traffic monitoring system. For example, aportion of the middlebox traffic flow stitching system 312 implementedat a network traffic monitoring system can received stitched trafficflows from a portion of the middlebox traffic flow stitching system 312implemented at the middlebox traffic flow segment collector 310.Subsequently, the middlebox traffic flow stitching system 312, e.g.implemented at the network traffic monitoring system can incorporate thestitched traffic flows into network traffic data generated by thenetwork traffic monitoring system.

In incorporating stitched traffic flows into network traffic data, themiddlebox traffic flow stitching system 312 can extend network trafficflows in the network traffic data based on the stitched traffic flows.Specifically, the middlebox traffic flow stitching system 312 can stitchalready stitched traffic flows extending through the middlebox 306 tothe client with other stitched traffic flows extending into the networkenvironment 300. More specifically, the middlebox traffic flow stitchingsystem 312 can stitch already stitched traffic flows through themiddlebox 306 with other traffic flows that extend from the server 304to other servers or nodes in the network environment 300. For example,the middlebox traffic flow stitching system 312 can stitch together atraffic flow extending from a network fabric to the server 304 with astitched traffic flow through the middlebox 306 to the client 302. Thiscan create a completed traffic flow from the network fabric to theclient 302 through the middlebox 306.

FIG. 4 illustrates a flowchart for an example method of stitchingtraffic flows passing through a middlebox. The method shown in FIG. 4 isprovided by way of example, as there are a variety of ways to carry outthe method. Additionally, while the example method is illustrated with aparticular order of blocks, those of ordinary skill in the art willappreciate that FIG. 4 and the blocks shown therein can be executed inany order and can include fewer or more blocks than illustrated.

Each block shown in FIG. 4 represents one or more steps, processes,methods or routines in the method. For the sake of clarity andexplanation purposes, the blocks in FIG. 4 are described with referenceto the network environment shown in FIG. 3.

At step 400, the middlebox traffic flow segment collector 310 collectsflow records of traffic flow segments at a middlebox in a networkenvironment corresponding to one or more traffic flows passing throughthe middlebox. The flow records can include one or more transactionidentifiers assigned to the traffic flow segments. The flow records ofthe traffic flow segments at the middlebox can be generated by themiddlebox and subsequently exported to the middlebox traffic flowsegment collector 310. More specifically, the flow records can beexported to the middlebox traffic flow segment collector 310 through theIPFIX protocol. The flow records can be exported to the middleboxtraffic flow segment collector 310 after each of the traffic flowsegments is established, e.g. through the middlebox. Alternatively, theflow records can be exported to the middlebox traffic flow segmentcollector 410 after a corresponding traffic flow of the traffic flowsegments is completed, e.g. through the middlebox.

At step 402, the middlebox traffic flow stitching system 312 identifiesflow directions of the traffic flow segments in the network environmentwith respect to the middlebox using the flow records. For example, themiddlebox traffic flow stitching system 312 can identify whether atraffic flow segment is passing from a client to the middlebox towards aserver using the flow records. In another example, the middlebox trafficflow stitching system 312 can identify whether a traffic flow segment ispassing from a server to the middlebox towards a client using the flowrecords. The middlebox traffic flow stitching system 312 can identifyflow directions of the traffic flow segments based on either or bothsources and destinations of the traffic flow segments included as partof the flow records. For example, the middlebox traffic flow stitchingsystem 312 can identify a flow direction of a flow segment based on anIP address of a server where the flow segment started and a SNIP port onthe middlebox that ends the flow segment at the middlebox.

At step 404, the middlebox traffic flow stitching system 312 stitchestogether the traffic flow segments to form a stitched traffic flow ofthe one or more traffic flows passing through the middlebox. Morespecifically, the traffic flow stitching system 312 can stitch togetherthe traffic flow segments to form a stitched traffic flow of the one ormore traffic flows based on one or more transaction identifiers assignedto the traffic flow segments and the flow directions of the traffic flowsegments in the network environment. For example, the traffic flowsegments sharing the same transaction identifier can be stitchedtogether based on the directions of the traffic flow segments form thestitched traffic flow, e.g. based on the flow records. Morespecifically, the one or more transaction identifiers assigned to thetraffic flow segments can be indicated by the flow records collected atstep 400 and subsequently used to stitch the traffic flow segmentstogether.

At step 406, the middlebox traffic flow stitching system 312incorporates the stitched traffic flow as part of network traffic datafor the network environment. Specifically, the stitched traffic flow canbe incorporated as part of identified traffic flows in the networkenvironment that are included as part of the network traffic data forthe network environment. For example, the stitched traffic flow can bestitched to traffic flows identified in a network fabric of the networkenvironment, as part of incorporating the stitched traffic flow withnetwork data for the network environment including the network fabric.

FIG. 5 shows an example middlebox traffic flow stitching system 500. Themiddlebox traffic flow stitching system 500 can function according to anapplicable system for stitching together traffic flow segments through amiddlebox to form a stitched traffic flow, such as the middlebox trafficflow stitching system 312 shown in FIG. 3. The middlebox traffic flowstitching system 500 can stitch together traffic flows using flowrecords collected or otherwise exported from a middlebox. Specifically,the middlebox traffic flow stitching system 500 can identify flowdirections of traffic flow segments and subsequently stitch togethertraffic flows based on transaction identifiers assigned to the trafficflow segments and flow directions of the traffic flow segments.

All of portions of the middlebox traffic flow stitching system 500 canbe implemented at an applicable collector for collecting flow recordsfrom a middlebox, such as the middlebox traffic flow segment collector310 shown in FIG. 3. Additionally, all or portions of the middleboxtraffic flow stitching system 500 can be implemented at a middlebox,e.g. as part of an agent. Further, all or portions of the middleboxtraffic flow stitching system 500 can be implemented at an applicablesystem for monitoring network traffic in a network environment, such asthe network traffic monitoring system 100 shown in FIG. 1.

The middlebox traffic flow stitching system 500 includes a flow recordshash table maintainer 502, a flow records hash table datastore 504, atraffic flow segment stitcher 506, and a completed flow identifier 508.The flow records hash table maintainer 502 functions to maintain a flowrecords hash table. The flow records hash table maintainer 502 canmaintain a hash table based on flow records collected from or otherwiseexported by a middlebox. In maintaining a flow records hash table, theflow records hash table maintainer 502 can generate and update one ormore flow records hash table stored in the flow records hash tabledatastore 504.

TABLE 1 T1 C -> VIP T1 IP -> Server T1 Server -> IP T1 VIP -> C T2C->VIP

Table 1, shown above, illustrates an example of a flow records hashtable maintained by the flow records hash table maintainer 502 andstored in the flow records hash table datastore 504. The example flowrecords hash table includes a plurality of entries. Each entrycorresponds to a traffic flow segment passing through a middlebox.Further, each entry includes a transaction identifier and a source anddestination identifier for each traffic flow segment. For example, thefirst entry corresponds to a traffic flow segment passing from theclient, C, to a port on the middlebox, VIP. Further in the example, thefirst entry includes a transaction identifier, T1, assigned to thetraffic flow segment, e.g. by a middlebox. In another example, thesecond entry corresponds to a second traffic flow segment passing fromthe middlebox, IP, to a server, signified by “Server” in the entry. Flowrecords hash tables can include entries with different transactionidentifiers corresponding to different traffic flows. Specifically, theexample flow records hash table has a first entry including a firsttransaction identifier T1 and a fifth entry including a secondtransaction identifier T2.

The traffic flow segment stitcher 506 functions to stitch togethertraffic flow segments at a middlebox to form a stitched traffic flowcorresponding to a traffic flow through the middlebox. Specifically, thetraffic flow segment stitcher 506 can stitch together traffic flowsegments using a flow records hash table, e.g. stored in the flowrecords hash table datastore 504. In using a flow records hash table tostitch together traffic flow segments, the traffic flow segment stitcher506 can stitch together traffic flows based on transaction identifiersincluded as part of entries corresponding to traffic flow segments inthe flow records hash table. For example, the traffic flow segmentstitcher 506 can stitch together a first traffic flow segmentcorresponding to the first entry in the example hash table and a secondtraffic flow segment corresponding to the second entry in the examplehash table based on both entries including the same transactionidentifier T1.

In using a flow records hash table to stitch together traffic flowsegments, the traffic flow segment stitcher 506 can group entries in thehash table to form grouped entries and subsequently use the groupedentries to stitch traffic flow segments. More specifically, the trafficflow segment stitcher 506 can group entries that share a transactionidentifier to form grouped entries. For example, the traffic flowsegment stitcher 506 can group the first four entries together based onthe entries all having the same transaction identifier T1. Subsequently,based on entries being grouped together to form grouped entries, thetraffic flow segment stitcher 506 can stitch together traffic flowscorresponding to the entries in the grouped entries. For example, thetraffic flow segment stitcher 506 can group the first four entries inthe example flow records hash table and subsequently stitch traffic flowsegments corresponding to the first four entries based on the groupingof the first four entries.

Further, in using a flow records hash table to stitch together trafficflow segments, the traffic flow segment stitcher 506 can identify flowdirections of the traffic flow segments based on corresponding entriesof the traffic flow segments in the flow records hash table. Morespecifically, the traffic flow segment stitcher 506 can identify flowdirections of traffic flow segments based on identifiers of sources anddestinations of the segments in corresponding entries in a flow recordshash table. For example, the traffic flow segment stitcher 506 canidentify that a traffic flow segment corresponding to the first entry inthe example hash table moves from a client to a middlebox based on theflow segment originating at the client and terminating at a VIP port atthe middlebox, as indicated by the first entry in the table.Subsequently, using flow directions of traffic flow segments identifiedfrom a flow records hash table, the traffic flow segment stitcher 506can actually stitch together the traffic flow segments. For example, thetraffic flow segment stitcher 506 can stitch a traffic flow segmentcorresponding to the fourth entry in the example hash table after atraffic flow segment corresponding to the third entry in the examplehash table.

The traffic flow segment stitcher 506 can stitch together traffic flowsegments based on both directions of the traffic flow segments, asidentified from corresponding entries in a flow records hash table, andtransaction identifiers included in the flow records hash table.Specifically, the traffic flow segment stitcher 506 can identify tostitch together traffic flow segments with corresponding entries in aflow records hash table that share a common transaction identifier. Forexample, the traffic flow segment stitcher 506 can determine to stitchtogether traffic flow segments corresponding to the first four entriesin the example flow records hash table based on the first four entriessharing the same transaction identifier T1. Additionally, the trafficflow segment stitcher 506 can determine an order to stitch togethertraffic flow segments based on flow directions of the traffic flowsegments identified from corresponding entries of the segments in a flowrecords hash table. For example, the traffic flow segment stitcher 506can determine to stitch together a third traffic flow segmentcorresponding to the third entry in the example hash table after asecond traffic flow segment corresponding to the second entry in theexample hash table based on flow directions of the segments identifiedfrom the entries.

The completed flow identifier 508 functions to identify a completedtraffic flow occurring through the middlebox. A completed traffic flowcan correspond to establishment of a connection between a client and aserver and vice versa. For example, a completed traffic flow can includea request transmitted from a client to a middlebox, and the requesttransmitted from the middlebox to a server. Further in the example, thecompleted traffic flow can include completion of the request from theclient to the server through the middlebox and completion of a responseto the request from the server to the client through the middlebox. Thecompleted flow identifier 508 can identify a completed flow based onflow records. More specifically, the completed flow identifier 508 canidentify a completed flow based on a flow records hash table. Forexample, the completed flow identifier 508 can identify the first fourentries form a completed flow based on both the first entry beginning atthe client and the last entry ending at the client, and all entrieshaving the same transaction identifier T1.

The traffic flow segment stitcher 506 can push or otherwise exporttraffic flow data for stitched traffic flows. More specifically, thetraffic flow segment stitcher 506 can export traffic flow data forincorporation with network traffic data for a network environment. Forexample, the traffic flow segment stitcher 506 can export traffic flowdata to the network traffic monitoring system 100, where the trafficflow data can be combined with network traffic data for a networkenvironment. The traffic flow segment stitcher 506 can push traffic flowdata based on identification of a completed traffic flow by thecompleted flow identifier 508. More specifically, the traffic flowsegment stitcher 506 can export traffic flow data indicating a stitchedtraffic flow of a completed traffic flow upon identification that thetraffic flow is actually a completed flow. This can ensure that data forstitched traffic flows is only pushed or otherwise provided when it isknown that the stitched traffic flows correspond to completed trafficflows.

The disclosure now turns to FIGS. 6 and 7, which illustrate examplenetwork devices and computing devices, such as switches, routers, loadbalancers, client devices, and so forth.

FIG. 6 illustrates an example network device 600 suitable for performingswitching, routing, load balancing, and other networking operations.Network device 600 includes a central processing unit (CPU) 604,interfaces 602, and a bus 610 (e.g., a PCI bus). When acting under thecontrol of appropriate software or firmware, the CPU 604 is responsiblefor executing packet management, error detection, and/or routingfunctions. The CPU 604 preferably accomplishes all these functions underthe control of software including an operating system and anyappropriate applications software. CPU 604 may include one or moreprocessors 608, such as a processor from the INTEL X86 family ofmicroprocessors. In some cases, processor 608 can be specially designedhardware for controlling the operations of network device 600. In somecases, a memory 606 (e.g., non-volatile RAM, ROM, etc.) also forms partof CPU 604. However, there are many different ways in which memory couldbe coupled to the system.

The interfaces 602 are typically provided as modular interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the network device 600. Among theinterfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like. In addition, various very high-speed interfaces may beprovided such as fast token ring interfaces, wireless interfaces,Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSIinterfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5Gcellular interfaces, CAN BUS, LoRA, and the like. Generally, theseinterfaces may include ports appropriate for communication with theappropriate media. In some cases, they may also include an independentprocessor and, in some instances, volatile RAM. The independentprocessors may control such communications intensive tasks as packetswitching, media control, signal processing, crypto processing, andmanagement. By providing separate processors for the communicationsintensive tasks, these interfaces allow the master microprocessor 604 toefficiently perform routing computations, network diagnostics, securityfunctions, etc.

Although the system shown in FIG. 6 is one specific network device ofthe present subject matter, it is by no means the only network devicearchitecture on which the present subject matter can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc., is often used.Further, other types of interfaces and media could also be used with thenetwork device 600.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 606) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc. Memory 606could also hold various software containers and virtualized executionenvironments and data.

The network device 600 can also include an application-specificintegrated circuit (ASIC), which can be configured to perform routingand/or switching operations. The ASIC can communicate with othercomponents in the network device 600 via the bus 610, to exchange dataand signals and coordinate various types of operations by the networkdevice 600, such as routing, switching, and/or data storage operations,for example.

FIG. 7 illustrates a computing system architecture 700 wherein thecomponents of the system are in electrical communication with each otherusing a connection 705, such as a bus. Exemplary system 700 includes aprocessing unit (CPU or processor) 710 and a system connection 705 thatcouples various system components including the system memory 715, suchas read only memory (ROM) 720 and random access memory (RAM) 725, to theprocessor 710. The system 700 can include a cache of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 710. The system 700 can copy data from the memory 715and/or the storage device 730 to the cache 712 for quick access by theprocessor 810. In this way, the cache can provide a performance boostthat avoids processor 710 delays while waiting for data. These and othermodules can control or be configured to control the processor 710 toperform various actions. Other system memory 715 may be available foruse as well. The memory 715 can include multiple different types ofmemory with different performance characteristics. The processor 710 caninclude any general purpose processor and a hardware or softwareservice, such as service 1 732, service 2 734, and service 3 736 storedin storage device 730, configured to control the processor 710 as wellas a special-purpose processor where software instructions areincorporated into the actual processor design. The processor 710 may bea completely self-contained computing system, containing multiple coresor processors, a bus, memory controller, cache, etc. A multi-coreprocessor may be symmetric or asymmetric.

To enable user interaction with the system 700, an input device 745 canrepresent any number of input mechanisms, such as a microphone forspeech, a touch-sensitive screen for gesture or graphical input,keyboard, mouse, motion input, speech and so forth. An output device 735can also be one or more of a number of output mechanisms known to thoseof skill in the art. In some instances, multimodal systems can enable auser to provide multiple types of input to communicate with the system700. The communications interface 740 can generally govern and managethe user input and system output. There is no restriction on operatingon any particular hardware arrangement and therefore the basic featureshere may easily be substituted for improved hardware or firmwarearrangements as they are developed.

Storage device 730 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 725, read only memory (ROM) 720, andhybrids thereof.

The storage device 730 can include services 732, 734, 736 forcontrolling the processor 710. Other hardware or software modules arecontemplated. The storage device 730 can be connected to the systemconnection 705. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 710, connection 705, output device735, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, medius, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of aset and indicates that one member of the set or multiple members of theset satisfy the claim. For example, claim language reciting “at leastone of A and B” means A, B, or A and B.

What is claimed is:
 1. A method comprising: collecting flow records oftraffic flow segments at a middlebox in a network environmentcorresponding to one or more traffic flows passing through themiddlebox, the flow records including one or more transactionidentifiers assigned to the traffic flow segments; identifying flowdirections of the traffic flow segments in the network environment withrespect to the middlebox using the flow records; stitching together thetraffic flow segments to form a stitched traffic flow of the one or moretraffic flows passing through the middlebox in the network environmentbased on the one or more transaction identifiers assigned to the trafficflow segments and the flow directions of the traffic flow segments inthe network environment with respect to the middlebox; and incorporatingthe stitched traffic flow as part of network traffic data for thenetwork environment.
 2. The method of claim 1, wherein the one or moretraffic flows pass through the middlebox directly between a client and aserver.
 3. The method of claim 1, wherein the one or more traffic flowspass through the middlebox to another middlebox in the networkenvironment.
 4. The method of claim 1, wherein the flow records arecollected from the middlebox as the middlebox exports the flow recordsusing an Internet Protocol Flow Information Export protocol.
 5. Themethod of claim 1, wherein the flow records include sources anddestinations of the traffic flow segments at the middlebox, and thesources and the destinations of the traffic flow segments are used tostitch together the traffic flow segments to form the stitched trafficflow at the middlebox.
 6. The method of claim 5, wherein the sources andthe destinations of the traffic flow segments are used to identify theflow directions of the traffic flow segments in the network environmentwith respect to the middlebox.
 7. The method of claim 1, furthercomprising: identifying whether the stitched traffic flow forms acomplete flow from the one or more traffic flows for a transactionbetween two entities in a network environment; and if it is determinedthat the stitched traffic flow forms the complete flow for thetransaction between the two entities in a network environment, thenpushing traffic flow data for the stitched traffic flow to a networktraffic monitoring system remote from the middlebox to incorporate thestitched traffic flow as part of the network traffic data for thenetwork environment.
 8. The method of claim 7, wherein the two entitiesinclude a client and a server.
 9. The method of claim 8, wherein thecomplete flow of the transaction between the client and the serverincludes a request sent from the client to the middlebox and included aspart of the traffic flow segments at the middlebox, the request sentfrom the middlebox to the server and included as part of the trafficflow segments at the middlebox, a response to the request sent from theserver to the middlebox and included as part of the traffic flowsegments at the middlebox, and the response to the request sent from themiddlebox to the client and included as part of the traffic flowsegments at the middlebox.
 10. The method of claim 1, furthercomprising: maintaining a hash table of the traffic flow segments at themiddlebox, the hash table including an entry corresponding to eachtraffic flow segment of the traffic flow segments, each entry includinga source and a destination of data in a corresponding traffic flowsegment of the entry and a transaction identification associated withthe corresponding traffic flow segment; and using the hash table of thetraffic flow segments at the middlebox to form the stitched traffic flowat the middlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and theflow directions of the traffic flow segments in the network environmentwith respect to the middlebox.
 11. The method of claim 10, furthercomprising identifying the flow directions of the traffic flow segmentsin the network environment using the hash table.
 12. The method of claim10, further comprising: grouping entries of the hash table based on thetraffic flow segments in the entries and the one or more transactionidentifiers associated with the traffic flow segments in the entries toform grouped entries of the hash table; and forming the stitched trafficflow based on the grouped entries of the hash table.
 13. The method ofclaim 1, wherein the stitched traffic flow is used to create anapplication dependency mapping as part of the network traffic data forthe network environment.
 14. The method of claim 1, wherein the stitchedtraffic flow is used to create a policy for the middlebox.
 15. A systemcomprising: one or more processors; and at least one computer-readablestorage medium having stored therein instructions which, when executedby the one or more processors, cause the one or more processors toperform operations comprising: collecting flow records of traffic flowsegments at a middlebox in a network environment corresponding to one ormore traffic flows passing between a client and a server directlythrough the middlebox, the flow records including one or moretransaction identifiers assigned to the traffic flow segments;identifying flow directions of the traffic flow segments in the networkenvironment with respect to the middlebox using the flow records;stitching together the traffic flow segments to form a stitched trafficflow of the one or more traffic flows passing through the middlebox inthe network environment based on the one or more transaction identifiersassigned to the traffic flow segments and the flow directions of thetraffic flow segments in the network environment with respect to themiddlebox; and incorporating the stitched traffic flow as part ofnetwork traffic data for the network environment.
 16. The system ofclaim 15, wherein the flow records include sources and destinations ofthe traffic flow segments at the middlebox, and the sources and thedestinations of the traffic flow segments are used to stitch togetherthe traffic flow segments to form the stitched traffic flow at themiddlebox.
 17. The system of claim 15, wherein the traffic flow segmentsincludes a request sent from the client to the middlebox and included aspart of the traffic flow segments at the middlebox, the request sentfrom the middlebox to the server and included as part of the trafficflow segments at the middlebox, a response to the request sent from theserver to the middlebox and included as part of the traffic flowsegments at the middlebox, and the response to the request sent from themiddlebox to the client and included as part of the traffic flowsegments at the middlebox.
 18. The system of claim 17, wherein theinstructions which, when executed by the one or more processors, furthercause the one or more processors to perform operations comprising:determining if the response to the request is sent directly from theserver to the client through the middlebox; and generating the networktraffic data to indicate the stitched traffic flow passes directly fromthe server to the client through the middlebox if it is determined thatthe response to the request is sent directly from the server to theclient through the middlebox.
 19. The system of claim 15, wherein theinstructions which, when executed by the one or more processors, furthercause the one or more processors to perform operations comprising:maintaining a hash table of the traffic flow segments at the middlebox,the hash table including an entry corresponding to each traffic flowsegment of the traffic flow segments, each entry including a source anda destination of data in a corresponding traffic flow segment of theentry and a transaction identification associated with the correspondingtraffic flow segment; and using the hash table of the traffic flowsegments at the middlebox to form the stitched traffic flow at themiddlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and theflow directions of the traffic flow segments in the network environmentwith respect to the middlebox.
 20. A non-transitory computer-readablestorage medium having stored therein instructions which, when executedby a processor, cause the processor to perform operations comprising:collecting flow records of traffic flow segments at a middlebox in anetwork environment corresponding to one or more traffic flows passingthrough the middlebox, the flow records including one or moretransaction identifiers assigned to the traffic flow segments;identifying flow directions of the traffic flow segments in the networkenvironment with respect to the middlebox using the flow records;stitching together the traffic flow segments to form a stitched trafficflow of the one or more traffic flows passing through the middlebox inthe network environment based on the one or more transaction identifiersassigned to the traffic flow segments and the flow directions of thetraffic flow segments in the network environment with respect to themiddlebox; and incorporating the stitched traffic flows as part of anapplication dependency mapping included as part of network traffic datafor the network environment.